In this paper, we study various technics to improve the performance, to reduce the computation cost and the required memory of a recognition system based on HMM. For the efficiency of the system, we first study the optimization of the number of HMM parameters according to training data. We experiment a temporal control of the phonetic transitions on lexical decoding task with a significant 5% improvement. Finally, a preliminary method selecting dynamically a sublexicon is studied in order to reduce the lexical decoding cost.